Computers have become an integral part of society. Every day people become more dependent on computers to facilitate both work and leisure activities. A significant drawback to computing technology is its “digital” nature as compared to the “analog” world in which it functions. Computers operate in a digital domain that requires discrete states to be identified in order for information to be processed. In simple terms, information generally must be input into a computing system with a series of “on” and “off” states (e.g., binary code). However, humans live in a distinctly analog world where occurrences are never completely black or white, but always seem to be in between shades of gray. Thus, a central distinction between digital and analog is that digital requires discrete states that are disjunct over time (e.g., distinct levels) while analog is continuous over time. As humans naturally operate in an analog fashion, computing technology has evolved to alleviate difficulties associated with interfacing humans to computers (e.g., digital computing interfaces) caused by the aforementioned temporal distinctions.
Handwriting, speech, and object recognition technologies have progressed dramatically in recent times, thereby enhancing effectiveness of digital computing interface(s). Such progression in interfacing technology enables a computer user to easily express oneself and/or input information into a system. As handwriting and speech are fundamental to a civilized society, these skills are generally learned by a majority of people as a societal communication requirement, established long before the advent of computers. Thus, no additional learning curve for a user is required to implement these methods for computing system interaction.
Effective handwriting, speech, and/or object recognition systems can be utilized in a variety of business and personal contexts to facilitate efficient communication between two or more individuals. For example, an individual at a conference can hand-write notes regarding information of interest, and thereafter quickly create a digital copy of such notes (e.g., scan the notes, photograph the notes with a digital camera, . . . ). A recognition system can be employed to recognize individual characters and/or words, and convert such handwritten notes to a document editable in a word processor. The document can thereafter be emailed to a second person at a distant location. Such a system can mitigate delays in exchanging and/or processing data, such as difficulty in reading an individual's handwriting, waiting for mail service, typing notes into a word processor, etc.
Conventional handwriting, speech, and/or object recognition systems and/or methodologies typically utilize one or more programs that are customized for particular actions and/or applications. For example, a customized program for determining identification of a particular character could employ a plurality of functions that search for particular features in order to identify such character. A program that identifies a “d” can first determine that a line of particular height exists, and thereafter determine that a single loop left of the line is present to facilitate identifying the “d”. While such customized programs have improved over time, empirical data suggests that statistical systems and/or methods outperform such customized programs.
Previously, statistical methods have not been employed in handwriting, speech, and/or object recognition systems due to limits in processing speed, bandwidth, and storage area, as effective statistical systems and/or methods utilized to recognize handwriting, speech, and/or objects require a substantial amount of labeled data to train a learning algorithm. As advances in technology have alleviated concerns regarding computing limits, an increase in popularity of statistical systems and/or methods has occurred. However, collecting labeled data utilized to train a learning algorithm employed in statistical recognition systems and/or methods remains a tedious and expensive task. For example, several instances of a same character must be collected and labeled as such character in order to effectively train a learning algorithm.
In view of at least the above, there exists a strong need in the art for a system and/or methodology that facilitates increasing learning speed and/or throughput with regard to character recognition.